TensorFlow 2.12: Inconsistent Accuracy with EarlyStopping Callback During Training
I'm building a feature where I'm attempting to set up I'm working on a text classification model using TensorFlow 2.12 and am experiencing an scenario with the `EarlyStopping` callback. The question arises when the training process halts prematurely without reaching the expected accuracy on the validation set. I have set up the model with the following configuration: ```python import tensorflow as tf from tensorflow.keras import layers, models, callbacks # Sample model architecture model = models.Sequential([ layers.Embedding(input_dim=10000, output_dim=128), layers.GlobalAveragePooling1D(), layers.Dense(64, activation='relu'), layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # EarlyStopping setup early_stopping = callbacks.EarlyStopping(monitor='val_accuracy', patience=3, restore_best_weights=True) # Sample dataset dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(32) # Training the model history = model.fit(dataset, validation_data=(X_val, y_val), epochs=20, callbacks=[early_stopping]) ``` Even though my validation accuracy is steadily improving, the training stops at epoch 6, where the validation accuracy is around 0.82, which is lower than I expected. The logs show messages like: ``` Epoch 6/20 1/1 [==============================] - 0s 27ms/step - loss: 0.2907 - accuracy: 0.8947 - val_loss: 0.3898 - val_accuracy: 0.8200 Epoch 6: early stopping ``` I have tried adjusting the `patience` parameter to a higher value and even changed the `monitor` parameter to `val_loss`, but the behavior remains the same. Additionally, I've verified that my validation set contains enough samples to provide a meaningful evaluation. Is there an underlying scenario with how the `EarlyStopping` callback interacts with my dataset or model? Any insights or suggestions would be greatly appreciated! For context: I'm using Python on Ubuntu 20.04. Any examples would be super helpful. I'm working in a Ubuntu 22.04 environment. Has anyone else encountered this?